# -*- coding: utf-8 -*- import time import json from tradingagents.agents.utils.output_filter import fix_common_llm_errors, validate_and_warn from tradingagents.agents.utils.prompts import get_neutral_debator_prompt def create_neutral_debator(llm, language: str = "zh-TW"): """ 建立一個中立的風險辯論員節點。 Args: llm: 用於生成回應的語言模型。 language: 報告語言 ('en' 或 'zh-TW') Returns: function: 一個代表中立辯論員節點的函式。 """ def neutral_node(state) -> dict: """中立辯論員節點的執行函式。""" risk_debate_state = state["risk_debate_state"] history = risk_debate_state.get("history", "") neutral_history = risk_debate_state.get("neutral_history", "") current_risky_response = risk_debate_state.get("current_risky_response", "") current_safe_response = risk_debate_state.get("current_safe_response", "") market_research_report = state["market_report"] sentiment_report = state["sentiment_report"] news_report = state["news_report"] fundamentals_report = state["fundamentals_report"] trader_decision = state["trader_investment_plan"] # Get language-specific prompt base_prompt = get_neutral_debator_prompt(language) if language == "en": prompt = f"""{base_prompt} 【Available Information】 - Trader Plan: {trader_decision} - Reports: {market_research_report}, {sentiment_report}, {news_report}, {fundamentals_report} - Debate History: {history} - Opponent Views: {current_risky_response}, {current_safe_response} Please provide your neutral risk analysis.""" else: prompt = f"""{base_prompt} 【可用資訊】 - 交易員計畫:{trader_decision} - 各類報告:{market_research_report}, {sentiment_report}, {news_report}, {fundamentals_report} - 辯論歷史:{history} - 對手觀點:{current_risky_response}, {current_safe_response} 請提供您的中立風險分析。""" response = llm.invoke(prompt) response.content = fix_common_llm_errors(response.content) validate_and_warn(response.content, "Neutral_Debator") if language == "en": argument = f"Neutral Analyst: {response.content}" else: argument = f"中立分析師:{response.content}" new_risk_debate_state = { "history": history + "\n" + argument, "neutral_history": neutral_history + "\n" + argument, "risky_history": risk_debate_state.get("risky_history", ""), "safe_history": risk_debate_state.get("safe_history", ""), "latest_speaker": "Neutral", "current_neutral_response": argument, "current_risky_response": risk_debate_state.get("current_risky_response", ""), "current_safe_response": risk_debate_state.get("current_safe_response", ""), "count": risk_debate_state["count"] + 1, } return {"risk_debate_state": new_risk_debate_state} return neutral_node